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MAEMOT: Pretrained MAE-Based Antiocclusion 3-D Multiobject Tracking for Autonomous Driving.

Xiaofei Zhang, Zhengping Fan, Ying Shen

    IEEE Transactions on Neural Networks and Learning Systems
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    PubMed
    Summary

    This study introduces MAEMOT, a novel method using a pretrained movement-constrained masked autoencoder (M-MAE) to overcome object occlusion challenges in 3-D multi-object tracking (MOT). MAEMOT effectively reconstructs lost data, improving tracking accuracy in complex traffic scenarios.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Artificial Intelligence

    Background:

    • Existing 3-D multi-object tracking (MOT) methods struggle with object occlusion in real-world traffic.
    • A fundamental challenge is overcoming perception data loss caused by occlusion.

    Purpose of the Study:

    • To develop a novel anti-occlusion 3-D MOT method named MAEMOT.
    • To address the limitations of current methods in handling occluded objects.

    Main Methods:

    • Development of a pretrained movement-constrained masked autoencoder (M-MAE) using a multistage transformer (MST) encoder and spatiotemporal motion decoder.
    • Implementation of a proposal-based geometric graph aggregation (PG2A) module for feature fusion.
    • Design of an object association module combining geometric and corner affinities.

    Main Results:

    • The M-MAE effectively predicts and reconstructs occluded point cloud data, maintaining inter-frame feature consistency.
    • The PG2A module refines region-of-interest components by fusing spatial features.
    • The combined approach robustly matches predicted occluded objects.

    Conclusions:

    • MAEMOT effectively overcomes occlusion interference in 3-D MOT.
    • The proposed method achieves improved performance in challenging traffic scenarios with occlusions.